Greater use of advanced analytics, and AI in particular, could produce a saving of between £150m and £200m in reduced warranty claims for car manufacturers, according to Deloitte

Greater use of advanced analytics, and AI in particular, could produce a saving of between £150m and £200m in reduced warranty claims for car manufacturers, according to Deloitte. The estimated saving is based on a typical market-leading original equipment manufacturer (OEM) with annual revenues of £100bn, incurring between one and two per cent of total annual revenue on warranty costs each year.

Warranty costs are the result of vehicles exhibiting lower-than-expected build quality. These issues tend to be the result of a design or manufacturing process, rather than isolated cases. Using AI to interpret, often handwritten, diagnostics can categorise quality concerns quickly and accurately when vehicles arrive for repair. By reducing the time taken to identify and implement a successful countermeasure, engineering time can also be freed up to focus on more business-critical work. Equally, individual vehicles generate a continuous stream of data, such as engine performance, back to the factory floor. In turn, this can inspire better decision-making, such as ways to improve operations before they become faults, or create new offerings at the development stage.

Michael Woodward, UK automotive partner at Deloitte, said:

“The automotive industry is under increasing pressure from alternatively-fuelled vehicles, disruptive technology, greater competition, and changing consumer demands. For OEMs, the challenge to maintain market share, profitability and, ultimately, the competitive advantage is set. Many are already re-examining their business models and investing heavily in technology to improve both efficiency and revenues. However, the potential of advanced analytics is still to be realised to its fullest, yet has the potential to completely transform the automotive industry.”

In addition to quality fixes, advanced analytics could also be used to limit factory downtime during machinery breakdowns, particularly as operations become increasingly complex. By identifying the causes of breakdowns, defects, and other holdups along the production line, outage can be predicted and countermeasures actioned proactively, rather than after the fact. Reducing downtime, unscheduled maintenance times, and improving overall efficiency could result in an output capacity increase of between two and four per cent each year.

Woodward continues:

“Whilst the potential rewards in adopting advanced analytics are excellent, investment is no guarantee of success. To deliver a successful project, OEMs should prioritise the business questions and challenges to focus on first, and why. At the same time, OEMs should continue to create an environment where talent can thrive and build their analytics around strategy, people and processes before making investments in data and technology.”